Detecting neuronal action potentials using a sparse signal representation

ABSTRACT

A system and method detect neuronal action potential signals from tissue responding to electrical stimulation signals. A sparse signal space model for a set of tissue response recordings has a signal space separable into a plurality of disjoint component manifolds including a neural action potential (NAP) component manifold corresponding to tissue response to electrical stimulation signals. A response measurement module is configured to: i. map a tissue response measurement signal into the sparse signal model space to obtain a corresponding sparse signal representation, ii. project the sparse signal representation onto the NAP component manifold to obtain a sparse NAP component representation, iii. when the sparse NAP component representation is greater than a minimum threshold value, report and recover a detected NAP signal in the tissue response measurement signal.

This application claims priority from U.S. Provisional PatentApplication 61/912,648, filed Dec. 6, 2013, which is incorporated hereinby reference in its entirety.

TECHNICAL FIELD

The present invention relates to detecting neuronal action potentialsignals from tissue responding to electrical stimulation signals,especially for hearing implant systems such as cochlear implant systems.

BACKGROUND ART

Most sounds are transmitted in a normal ear as shown in FIG. 1 throughthe outer ear 101 to the tympanic membrane (eardrum) 102, which movesthe bones of the middle ear 103 (malleus, incus, and stapes) thatvibrate the oval window and round window openings of the cochlea 104.The cochlea 104 is a long narrow duct wound spirally about its axis forapproximately two and a half turns. It includes an upper channel knownas the scala vestibuli and a lower channel known as the scala tympani,which are connected by the cochlear duct. The cochlea 104 forms anupright spiraling cone with a center called the modiolus where thespiral ganglion cells of the acoustic nerve 113 reside. In response toreceived sounds transmitted by the middle ear 103, the fluid-filledcochlea 104 functions as a transducer to generate electric pulses whichare transmitted to the cochlear nerve 113, and ultimately to the brain.

Hearing is impaired when there are problems in the ability to transduceexternal sounds into meaningful action potentials along the neuralsubstrate of the cochlea 104. To improve impaired hearing, auditoryprostheses have been developed. For example, when the impairment isassociated with the cochlea 104, a cochlear implant with an implantedstimulation electrode can electrically stimulate auditory nerve tissuewith small currents delivered by multiple electrode contacts distributedalong the electrode.

In some cases, hearing impairment can be addressed by a cochlear implant(CI), a brainstem-, midbrain- or cortical implant that electricallystimulates auditory nerve tissue with small currents delivered bymultiple electrode contacts distributed along an implant electrode. Forcochlear implants, the electrode array is inserted into the cochlea. Forbrainstem, midbrain and cortical implants, the electrode array islocated in the auditory brainstem, midbrain or cortex, respectively.

FIG. 1 shows some components of a typical cochlear implant system wherean external microphone provides an audio signal input to an externalsignal processor 111 which implements one of various known signalprocessing schemes. For example, signal processing approaches that arewell-known in the field of cochlear implants include continuousinterleaved sampling (CIS) digital signal processing, channel specificsampling sequences (CSSS) digital signal processing (as described inU.S. Pat. No. 6,348,070, incorporated herein by reference), spectralpeak (SPEAK) digital signal processing, fine structure processing (FSP)and compressed analog (CA) signal processing.

The processed signal is converted by the external signal processor 111into a digital data format, such as a sequence of data frames, fortransmission by an external coil 107 into a receiving stimulatorprocessor 108. Besides extracting the audio information, the receiverprocessor in the stimulator processor 108 may perform additional signalprocessing such as error correction, pulse formation, etc., and producesa stimulation pattern (based on the extracted audio information) that issent through electrode lead 109 to an implanted electrode array 110.Typically, the electrode array 110 includes multiple stimulationcontacts 112 on its surface that provide selective electricalstimulation of the cochlea 104.

Generally, there is a need to obtain data from the implanted componentsof a cochlear implant. Such data collection enables detection andconfirmation of the normal operation of the device, and allowsstimulation parameters to be optimized to suit the needs of individualrecipients. This includes data relating to the response of the auditorynerve to stimulation, which is of particular relevance to the presentinvention. Thus, regardless of the particular configuration, cochlearimplants generally have the capability to communicate with an externaldevice such as for program upgrades and/or implant interrogation, and toread and/or alter the operating parameters of the device.

Typically, following the surgical implantation of a cochlear implant,the implant is fitted or customized to conform to the specific recipientdemands. This involves the collection and determination ofpatient-specific parameters such as threshold levels (T levels) andmaximum comfort levels (C levels) for each stimulation channel.Essentially, the procedure is performed manually by applying stimulationpulses for each channel and receiving an indication from the implantrecipient as to the level and comfort of the resulting sound. Forimplants with a large number of channels for stimulation, this processis quite time consuming and rather subjective as it relies heavily onthe recipient's Subjective impression of the stimulation rather than anyobjective measurement.

This approach is further limited in the case of children andprelingually or congenitally deaf patients who are unable to supply anaccurate impression of the resultant hearing sensation, and hencefitting of the implant may be suboptimal. In such cases anincorrectly-fitted cochlear implant may result in the recipient notreceiving optimum benefit from the implant, and in the cases ofchildren, may directly hamper the speech and hearing development of thechild. Therefore, there is a need to obtain objective measurements ofpatient-specific data, especially in cases where an accurate subjectivemeasurement is not possible.

One proposed method of interrogating the performance of an implantedcochlear implant and making objective measurements of patient-specificdata such as T and C levels is to directly measure the response of theauditory nerve to an electrical stimulus. To collect information aboutthe electrode-nerve interface, a commonly used objective measurement isbased on the measurement of Neural Action Potentials (NAPs) such as theelectrically-evoked Compound Action Potential (eCAP), as described byGantz et al., Intraoperative Measures of Electrically Evoked AuditoryNerve Compound Action Potentials, American Journal of Otology 15(2):137-144 (1994), which is incorporated herein by reference. In thisapproach, the recording electrode is usually placed at the scala tympaniof the inner ear. The overall response of the auditory nerve to anelectrical stimulus is measured typically very close to the position ofthe nerve excitation. This neural response is caused by thesuper-position of single neural responses at the outside of the auditorynerve membranes. The response is characterized by the amplitude betweenthe minimum voltage (this peak is called typically N1) and the maximumvoltage (peak is called typically P2). The amplitude of the eCAP at themeasurement position is between 10 μV and 1800 μV. One eCAP recordingparadigm is a so-called “amplitude growth function,” as described byBrown et al., Electrically Evoked Whole Nerve Action Potentials InIneraid Cochlear Implant Users: Responses To Different StimulatingElectrode Configurations And Comparison To Psychophysical Responses,Journal of Speech and Hearing Research, vol. 39:453-467 (June 1996),which is incorporated herein by reference. This function is the relationbetween the amplitude of the stimulation pulse and the peak-to-peakvoltage of the eCAP. Another clinically used recording paradigm is theso called “recovery function” in which stimulation is achieved with twopulses with varying interpulse intervals. The recovery function as therelation of the amplitude of the second eCAP and the interpulse intervalallows conclusions to be drawn about the refractory properties andparticular properties concerning the time resolution of the auditorynerve.

Detecting NAPs such as eCAPs is based on an analysis of an obtainedmeasurement recording (R) which can be understood as a signal mixturecontaining the desired NAPs (A), artifacts due to the stimulation (B)and other sources (C) and noise (D). A linear model of this signalmixture is:R=A+B+C+D

State-of-the-art NAP measurement systems apply special recordingsequences to reduce the unwanted artifacts and the noise present duringthe measurement. The stimulation artifact (B) is partially removed fromthe recording (R) by different measurement paradigms such as“alternating stimulation” (Eisen M D, Franck K H: “Electrically EvokedCompound Action Potential Amplitude Growth Functions and HiResolutionProgramming Levels in Pediatric CII Implant Subjects.” Ear & Hearing2004, 25 (6):528-538; which is incorporated herein by reference in itsentirety), “masker probe” (Brown C, Abbas P, Gantz B: “Electricallyevoked whole-nerve action potentials: data from human cochlear implantusers.” The Journal of the Acoustical Society of America 1990, 88(3):1385-1391; Miller C A, Abbas P J, Brown C J: An improved method ofreducing stimulus artifact in the electrically evoked whole-nervepotential. Ear & Hearing 2000, 21 (4):280-290; both of which areincorporated herein by reference in their entireties), “tri-phasicstimulation” (Zimmerling M: “Messung des elektrisch evoziertenSummenaktionspotentials des Hörnervs bei Patienten mit einemCochlea-Implantat.” In PhD thesis Universität Innsbruck, Institut fürAngewandte Physik; 1999; Schoesser H, Zierhofer C, Hochmair E S.“Measuring electrically evoked compound action potentials usingtriphasic pulses for the reduction of the residual stimulationartefact,” In: Conference on implantable auditory prostheses; 2001; bothof which are incorporated herein by reference in their entireties), and“scaled template” (Miller C A, Abbas P J, Rubinstein J T, Robinson B,Matsuoka A, Woodworth G: Electrically evoked compound action potentialsof guinea pig and cat: responses to monopolar, monophasic stimulation.Hearing Research 1998, 119 (1-2):142-154; which is incorporated hereinby reference in its entirety). Artifacts due to other sources (C) arepartially removed by a “zero amplitude template” (Brown et al. 2000).The noise (D) is reduced by repeated measurements, averaging over therepeated recordings reduces the noise level by √N for N repetitions.

These special recording sequences result in a processed recording (R′)with a reduced noise floor (D′) and remaining artifacts (B′ and C′)which in most cases are reduced in amplitude. Some recording sequencesalso result in an altered NAP response (A′), for example the “maskerprobe” paradigm (Westen, A. A.; Dekker, D. M. T.; Briaire, J. J. &Frijns, J. H. M. “Stimulus level effects on neural excitation and eCAPamplitude.” Hear Res, 2011, 280, 166-176; which is incorporated hereinby reference in its entirety).

To automatically detect a NAP response in the resulting recording (R′)one commonly used technique is known as “template matching” (SmartNRT asused by Advanced Bionics; Arnold, L. & Boyle, P. “SmartNRI: algorithmand mathematical basis.” Proceedings of 8th EFAS Congress/10th Congressof the German Society of Audiology, 2007; which is incorporated hereinby reference in its entirety). First an additional de-noising of therecording (R′) is performed by calculating correlations with basisfunctions predefined by a principal component analysis and performingweighted summation, resulting in a recording (R″) with reduced noise(see U.S. Pat. No. 7,447,549; which is incorporated herein by referencein its entirety). Then an artifact model (B_(Model)+C_(Model))representing the sum of two decaying exponentials is fitted to thispost-processed recording (R″) and with a strength of response metric(SOR=(R″−B_(Model)−C_(Model))/noise) a threshold is determined to detecta possible NAP (A) (U.S. Pat. No. 7,818,052; which is incorporatedherein by reference in its entirety).

Another approach to automatically detect a NAP response in the resultingrecording (R′) is known as expert system (AutoNRT™ as used by CochlearLtd.; Botros, A.; van Dijk, B. & Killian, M. “AutoNRT™: An automatedsystem that measures ECAP thresholds with the Nucleus® Freedom™ cochlearimplant via machine intelligence” Artificial Intelligence in Medicine,2007, 40, 15-28; which is incorporated herein by reference in itsentirety). The expert system used is a combination of a templatematching and a decision tree classifier (U.S. Patent Publication US20080319508 A1; which is incorporated herein by reference in itsentirety). The template matching classifier computes the correlationwith a NAP (A) template and a NAP plus stimulation artifact (A+B)template. The decision tree uses the following six parameters:

-   -   N1-P1 amplitude for NAP typically latencies    -   noise level    -   ratio N1-P1 amplitude to noise level    -   correlation with NAP (A) template    -   correlation with NAP plus stimulation artifact (A+B) template    -   correlation between this measurement (R) and a previous        measurement at a lower stimulation amplitude.        Two different decision tree classifiers were learned with a C5.0        decision tree algorithm. For the case where no NAP (A) was        detected at lower stimulation levels, the stimulation level was        increased and a decision tree with a low false positive rate was        used to determine the presence of a NAP (A). For the case where        a NAP (A) was detected, the stimulation level was reduced and a        decision tree with a low overall error rate was used to evaluate        the presence of a NAP (A).

An established working hypothesis is that neurosensory systems areperforming a highly optimized signal analysis using a sparserepresentation (see for example B. Olshausen and D. Field, “Sparsecoding of sensory inputs,” Curr Opin Neurobiol, vol. 14, no. 4, pp.481-487, 2004, incorporated herein by reference in its entirety). Such asignal model is important in the context of analysis, estimation andautomatic detection of a signal. The earliest theoretical signalanalysis model, proposed by Fourier (J. B. J. Fourier, Théorieanalytique de la chaleur (The Analytical Theory of Heat). Paris: F.Didot, 1822, incorporated herein by reference in its entirety), analyzesthe frequency content of a signal using the expansion of functions intoa weighted sum of sinusoids. Gabor (D. Gabor, “Theory ofcommunications,” Journal of Institute of Electrical Engineers, vol. 93,no. III-26, pp. 429-457, 1946, incorporated herein by reference in itsentirety) extended this signal model by using shifted and modulatedtime-frequency atoms which analyze the signal in the frequency as wellas in the time dimension. The wavelet signal model, a furtherimprovement presented by Morlet et al. (J. Morlet, G. Arens, I.Fourgeau, and D. Giard, “Wave propagation and sampling theory,”Geophysics, vol. 47, no. 2, pp. 203-236, 1982, incorporated herein byreference in its entirety), uses time-frequency atoms that are scaleddependent on their center frequency. This yields an analysis of thetime-frequency plane with a non-uniform tiling. However, thetime-frequency atoms used in these signal models normally do not assumean underlying signal structure. As the performance of subsequentdetection algorithms depends strongly on how well the fundamentalfeatures of a signal are captured, it is favorable to use time-frequencyatoms that are specialized to the applied signal class and inherentlyexhibit the property of a sparse representation. To derive such a datadependent sparse signal-model, several algorithms have been proposed,for example, but not limited to: MOD (K. Engan, S. O. Aase, and J. H.Husøy, “Method of optimal directions for frame design”, Proc. ICASSP,Vol. 5, pp. 2443-2446, 1999, incorporated herein by reference in itsentirety) or K-SVD (U.S. Pat. No. 8,165,215, incorporated herein byreference in its entirety).

SUMMARY

Embodiments of the present invention are directed to a system and methodto detect neuronal action potential signals from tissue responding toelectrical stimulation signals. A sparse signal space model for a set oftissue response recordings has a signal space separable into a pluralityof disjoint component manifolds including a neural action potential(NAP) component manifold corresponding to tissue response to electricalstimulation signals. A response measurement module is configured to: i.map a tissue response measurement signal into the sparse signal modelspace to obtain a corresponding sparse signal representation, ii.project the sparse signal representation onto the NAP component manifoldto obtain a sparse NAP component representation, iii. report a detectedNAP signal in the tissue response measurement signal when the signalenergy of the sparse NAP component representation is greater than aminimum threshold value.

Based on the signal mixture R=A+B+C+D (as described above), the responsemeasurement module may be further configured to project, recover andreport at least one other detected signal for at least one othercomponent manifold in the model signal space, such as a stimulationartifact signal B. The detected NAP signal A may specifically be anelectrically-evoked compound action potential (eCAP) signal. The sparsesignal space model may specifically be a MOD or K-SVD trained model. TheNAP component manifold may be constrained by a NAP signal model. And theminimum threshold value may be a fixed constant value, or a variablefunction of one or more components in the tissue response measurementsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows anatomical structures of a human ear having a cochlearimplant system.

FIG. 2 shows various components in a system for measuring neural actionpotential (NAP) signals from tissue responding to electrical stimulationsignals according to one specific embodiment of the present invention.

FIG. 3 shows the functional steps in a method of measuring neural actionpotential (NAP) signals from tissue responding to electrical stimulationsignals according to one specific embodiment of the present invention.

DETAILED DESCRIPTION

Instead of using complex detection algorithms such as template matchingor machine-learned expert systems such as decision tree classifiers torecognize possible NAPs directly in the tissue response measurementrecording, embodiments of the present invention maps the recording intoa sparse signal space using for example a MOD or K-SVD trained signalmodel to obtain a sparse signal representation which allows a robust andcomputationally inexpensive signal detection and classification ofpossible NAPs and signal artifacts within this signal space.

FIG. 2 shows various functional blocks in a system for measuring neuralaction potential (NAP) signals from tissue responding to electricalstimulation signals and FIG. 3 shows the functional steps in a method ofmeasuring neural action potential (NAP) signals from tissue respondingto electrical stimulation signals according to embodiments of thepresent invention. Response measurement module 201 contains acombination of software and hardware for generating electricalstimulation pulses for the target neural tissue and recording andanalyzing the NAPs. For example, the response measurement module 201 maybe based on a Research Interface Box (RIB) II system manufactured at theUniversity of Technology Innsbruck, Austria which may include a personalcomputer equipped with a National Instruments digital IO card, a RIB IIisolation box, and a communications cable between IO card and RIB IIbox. The electrical stimulation pulses are transmitted from the responsemeasurement module 201 through a control interface 202 to an externaltransmitter 203 which transmits them through the skin to implantelectrodes to the target neural tissue. The NAP responses are recordedat the implant electrodes and transmitted via the external transmitter203 through the control interface 202 to the response measurement module201.

Initially, a sparse signal space model S is trained for a set of tissueresponse recordings in a form r=a+b+c+d, where r∈

^(N) is an individual tissue response recording representing a signalmixture, a∈

^(N) is a neural action potential (NAP) component of r, b|

^(N) is a stimulation artifact component of r, c∈

^(N) is an other source artifact component of r, d∈

^(N) is a noise component of r, where the sparse signal model S:

^(N)→

^(M) such that a sparse signal representation r_(s)=ƒ_(s)(r) withmin_(r) _(s) ∥r_(s)∥₀, and

-   -   i. a_(s)=ƒ_(s)(a), a_(s)∈A=        ^(α)⊂S, α<M,    -   ii. b_(s)=ƒ_(s)(b), b_(s)∈B=        ^(β)⊂S, β<M,    -   iii. c_(s)=ƒ_(s)(c), c_(s)∈C=        ^(γ)⊂S, γ<M,        with A∩B=Ø, A∩C=Ø, B∩C=Ø and α+β+γ<M. The minimum of a function        with respect to a variable x is denoted as min_(x) ƒ(x). The L0        norm, which corresponds to the number of non-zero elements, is        denoted as ∥⋅∥₀. That is, the sparse signal space S is separable        into multiple disjoint component manifolds (A, B, C). Training        of the sparse signal space model S only needs to be done once        with a sufficiently large number of known tissue response        recordings and for each of the component manifolds.

The response measurement module 201 then accesses the sparse signalspace model, step 301, and derives a sparse signal representation r_(s)for the tissue response measurement signal r using the predefined sparsesignal space model S, step 302. The response measurement module 201 thenprojects the sparse representation r_(s) onto all predefined manifolds,step 303; for example, projecting the sparse representation r_(s) ontothe NAP component manifold A to obtain a sparse representation a_(s) ofa possible NAP, step 304. The response measurement module 201 thenreports if a predefined signal was present in the tissue responsemeasurement signal r when the signal energy of the sparse representationis greater than a minimum threshold value energy; e.g., a NAP componenta is reported if the derived ∥a_(s)∥>a_(thr), step 305.

If the stimulation artifact signal b is desired, then the responsemeasurement module 201 projects sparse representation r_(s) into thestimulation artifact manifold B, and likewise for source artifactcomponent signal c and the noise component d. This system allows ameasurement analysis using just computationally inexpensive projections.That reduces the computational complexity considerably, and furthermore,operating within the sparse signal space is very efficient since many ofthe signal coefficients are zero. Furthermore this system mimics thesignal processing of neurosensory systems that are optimized to performin a robust manner.

Once the projection into the predefined sparse signal space has beendone, the needed energy to detect a component signal a, b, c or d can becalculated or looked up in a table. For example, a look up table maystore the energy for the associated NAP signal of the NAP componentmanifold in the sparse signal space. If the energy level of the signalcomponent is above some minimum threshold value, then the NAP signal hasbeen recovered. This energy threshold may be a fixed level, or in someembodiments, it may be a variable function of one or more of thecomponents in the tissue response measurement signal. For example, ifthe stimulation artifact b has a relatively high signal energy, thatsuggests that the reference electrode contact has high impedance and mayneed to be checked. It also suggests that the estimate of the NAP signalneeds to be done very carefully, and so the energy threshold for the NAPsignal may accordingly be increased.

Embodiments of the invention may be implemented in part in anyconventional computer programming language. For example, preferredembodiments may be implemented in a procedural programming language(e.g., “C”) or an object oriented programming language (e.g., “C++”,Python). Alternative embodiments of the invention may be implemented aspre-programmed hardware elements, other related components, or as acombination of hardware and software components.

Embodiments also can be implemented in part as a computer programproduct for use with a computer system. Such implementation may includea series of computer instructions fixed either on a tangible medium,such as a computer readable medium (e.g., a diskette, CD-ROM, ROM, orfixed disk) or transmittable to a computer system, via a modem or otherinterface device, such as a communications adapter connected to anetwork over a medium. The medium may be either a tangible medium (e.g.,optical or analog communications lines) or a medium implemented withwireless techniques (e.g., microwave, infrared or other transmissiontechniques). The series of computer instructions embodies all or part ofthe functionality previously described herein with respect to thesystem. Those skilled in the art should appreciate that such computerinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Furthermore, suchinstructions may be stored in any memory device, such as semiconductor,magnetic, optical or other memory devices, and may be transmitted usingany communications technology, such as optical, infrared, microwave, orother transmission technologies. It is expected that such a computerprogram product may be distributed as a removable medium withaccompanying printed or electronic documentation (e.g., shrink wrappedsoftware), preloaded with a computer system (e.g., on system ROM orfixed disk), or distributed from a server or electronic bulletin boardover the network (e.g., the Internet or World Wide Web). Of course, someembodiments of the invention may be implemented as a combination of bothsoftware (e.g., a computer program product) and hardware. Still otherembodiments of the invention are implemented as entirely hardware, orentirely software (e.g., a computer program product).

Although various exemplary embodiments of the invention have beendisclosed, it should be apparent to those skilled in the art thatvarious changes and modifications can be made which will achieve some ofthe advantages of the invention without departing from the true scope ofthe invention.

What is claimed is:
 1. A method for detecting neuronal action potentialsignals from tissue responding to electrical stimulation signals, andproviding stimulation to address hearing impairment, the methodcomprising: transmitting, via an external transmitter, stimulationpulses to a cochlear implant, the cochlear implant including electrodesfor providing the stimulation pulses to neural tissue and detecting atissue response measurement signal; receiving, via the externaltransmitter, the tissue response measurement signal from the cochlearimplant; using a computer to perform the steps of: accessing a sparsesignal space model for a set of tissue response recordings, the modelhaving a signal space separable into a plurality of disjoint componentmanifolds including: a neural action potential (NAP) component manifoldcorresponding to tissue response to electrical stimulation signals, astimulation artifact component manifold corresponding to artifacts dueto the electrical stimulation signals, a source artifact componentmanifold corresponding to artifacts due to sources other than theelectrical stimulation signals, and a noise artifact component manifold;mapping the tissue response measurement signal into the model signalspace to obtain a corresponding sparse signal representation; projectingthe sparse signal representation onto the NAP component manifold toobtain a sparse NAP component representation; and when the sparse NAPcomponent representation is greater than a minimum threshold value,reporting a detected NAP signal in the tissue response measurementsignal; and determining an operating parameter for the cochlear implantbased on the detected NAP signal; transmitting, via the externaltransmitter, the operating parameter to the cochlear implant; andproviding, by the cochlear implant, stimulation pulses to theelectrodes, the pulses based, at least in part, on the operatingparameter.
 2. The method according to claim 1, further comprising:reperforming the steps of projecting, recovering and reporting for atleast one other component manifold in the model signal space.
 3. Themethod according to claim 2, wherein the at least one other componentmanifold includes a stimulation signal artifact component manifold andthe detected signal is a stimulation artifact signal.
 4. The methodaccording to claim 1, wherein the detected NAP signal is anelectrically-evoked compound action potential (eCAP) signal.
 5. Themethod according to claim 1, wherein the sparse signal space model is aMOD or K-SVD trained model.
 6. The method according to claim 1, whereinthe NAP component manifold is constrained by a NAP signal model.
 7. Themethod according to claim 1, wherein the minimum threshold value is afixed constant value.
 8. The method according to claim 1, wherein theminimum threshold value is a variable function of one or more componentsin the tissue response measurement signal.